def _valid_epoch(self):
# Change the network to evaluation mode
self.model.eval()
self.val_data_loader.dataset.reset_seed(0)
num_data = 0
hit_ratio_meter, feat_match_ratio, loss_meter, rte_meter, rre_meter = AverageMeter(
), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
data_timer, feat_timer, matching_timer = Timer(), Timer(), Timer()
tot_num_data = len(self.val_data_loader.dataset)
if self.val_max_iter > 0:
tot_num_data = min(self.val_max_iter, tot_num_data)
data_loader_iter = self.val_data_loader.__iter__()
for batch_idx in range(tot_num_data):
data_timer.tic()
input_dict = data_loader_iter.next()
data_timer.toc()
# pairs consist of (xyz1 index, xyz0 index)
feat_timer.tic()
sinput0 = ME.SparseTensor(
input_dict['sinput0_F'], coords=input_dict['sinput0_C']).to(self.device)
F0 = self.model(sinput0).F
sinput1 = ME.SparseTensor(
input_dict['sinput1_F'], coords=input_dict['sinput1_C']).to(self.device)
F1 = self.model(sinput1).F
feat_timer.toc()
matching_timer.tic()
xyz0, xyz1, T_gt = input_dict['pcd0'], input_dict['pcd1'], input_dict['T_gt']
xyz0_corr, xyz1_corr = self.find_corr(xyz0, xyz1, F0, F1, subsample_size=5000)
T_est = te.est_quad_linear_robust(xyz0_corr, xyz1_corr)
loss = corr_dist(T_est, T_gt, xyz0, xyz1, weight=None)
loss_meter.update(loss)
rte = np.linalg.norm(T_est[:3, 3] - T_gt[:3, 3])
rte_meter.update(rte)
rre = np.arccos((np.trace(T_est[:3, :3].t() @ T_gt[:3, :3]) - 1) / 2)
if not np.isnan(rre):
rre_meter.update(rre)
hit_ratio = self.evaluate_hit_ratio(
xyz0_corr, xyz1_corr, T_gt, thresh=self.config.hit_ratio_thresh)
hit_ratio_meter.update(hit_ratio)
feat_match_ratio.update(hit_ratio > 0.05)
matching_timer.toc()
num_data += 1
torch.cuda.empty_cache()
if batch_idx % 100 == 0 and batch_idx > 0:
logging.info(' '.join([
f"Validation iter {num_data} / {tot_num_data} : Data Loading Time: {data_timer.avg:.3f},",
f"Feature Extraction Time: {feat_timer.avg:.3f}, Matching Time: {matching_timer.avg:.3f},",
f"Loss: {loss_meter.avg:.3f}, RTE: {rte_meter.avg:.3f}, RRE: {rre_meter.avg:.3f},",
f"Hit Ratio: {hit_ratio_meter.avg:.3f}, Feat Match Ratio: {feat_match_ratio.avg:.3f}"
]))
data_timer.reset()
logging.info(' '.join([
f"Final Loss: {loss_meter.avg:.3f}, RTE: {rte_meter.avg:.3f}, RRE: {rre_meter.avg:.3f},",
f"Hit Ratio: {hit_ratio_meter.avg:.3f}, Feat Match Ratio: {feat_match_ratio.avg:.3f}"
]))
return {
"loss": loss_meter.avg,
"rre": rre_meter.avg,
"rte": rte_meter.avg,
'feat_match_ratio': feat_match_ratio.avg,
'hit_ratio': hit_ratio_meter.avg
}
def _valid_epoch(self):
# Change the network to evaluation mode
self.model.eval()
self.val_data_loader.dataset.reset_seed(0)
num_data = 0
hit_ratio_meter, feat_match_ratio, loss_meter, rte_meter, rre_meter = AverageMeter(
), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
data_timer, feat_timer, matching_timer = Timer(), Timer(), Timer()
tot_num_data = len(self.val_data_loader.dataset)
if self.val_max_iter > 0:
tot_num_data = min(self.val_max_iter, tot_num_data)
data_loader_iter = self.val_data_loader.__iter__()
for batch_idx in range(tot_num_data):
data_timer.tic()
input_dict = data_loader_iter.next()
data_timer.toc()
# pairs consist of (xyz1 index, xyz0 index)
feat_timer.tic()
coords = input_dict['sinput0_C'].to(self.device)
sinput0 = ME.SparseTensor(
input_dict['sinput0_F'].to(self.device),
coordinates=input_dict['sinput0_C'].to(self.device).type(torch.float))
F0 = self.model(sinput0).F
sinput1 = ME.SparseTensor(
input_dict['sinput1_F'].to(self.device),
coordinates=input_dict['sinput1_C'].to(self.device).type(torch.float))
F1 = self.model(sinput1).F
feat_timer.toc()
matching_timer.tic()
xyz0, xyz1, T_gt = input_dict['pcd0'], input_dict['pcd1'], input_dict['T_gt']
xyz0_corr, xyz1_corr = self.find_corr(xyz0, xyz1, F0, F1, subsample_size=5000)
if False:
from sklearn.decomposition import PCA
import open3d as o3d
pc0 = o3d.geometry.PointCloud()
pc0.points = o3d.utility.Vector3dVector(xyz0.numpy())
pca = PCA(n_components=3)
colors = pca.fit_transform(torch.cat((F0, F1), axis=0).cpu().numpy())
colors -= colors.min()
colors /= colors.max()
pc0.colors = o3d.utility.Vector3dVector(colors[0:F0.shape[0]])
o3d.io.write_point_cloud("pc0.ply", pc0)
pc0.transform(T_gt.numpy())
o3d.io.write_point_cloud("pc0_trans.ply", pc0)
pc1 = o3d.geometry.PointCloud()
pc1.points = o3d.utility.Vector3dVector(xyz1.numpy())
pc1.colors = o3d.utility.Vector3dVector(colors[F0.shape[0]:])
o3d.io.write_point_cloud("pc1.ply", pc1)
ind_0 = input_dict['correspondences'][:, 0].type(torch.long)
ind_1 = input_dict['correspondences'][:, 1].type(torch.long)
pc1.points = o3d.utility.Vector3dVector(xyz1[ind_1].numpy())
pc1.colors = o3d.utility.Vector3dVector(
colors[F0.shape[0]:][ind_1])
o3d.io.write_point_cloud("pc1_corr.ply", pc1)
pc0.points = o3d.utility.Vector3dVector(xyz0[ind_0].numpy())
pc0.colors = o3d.utility.Vector3dVector(colors[:F0.shape[0]][ind_0])
pc0.transform(T_gt.numpy())
o3d.io.write_point_cloud("pc0_trans_corr.ply", pc0)
import pdb
pdb.set_trace()
#pc0.points = o3d.utility.Vector3dVector(xyz0_corr.numpy())
# pc0.transform(T_gt.numpy())
#o3d.io.write_point_cloud("xyz0_corr_trans.ply" , pc0)
#
#pc0.points = o3d.utility.Vector3dVector(xyz1_corr.numpy())
#o3d.io.write_point_cloud("xyz1_corr_trans.ply" , pc0)
T_est = te.est_quad_linear_robust(xyz0_corr, xyz1_corr)
loss = corr_dist(T_est, T_gt, xyz0, xyz1, weight=None)
loss_meter.update(loss)
rte = np.linalg.norm(T_est[:3, 3] - T_gt[:3, 3])
rte_meter.update(rte)
rre = np.arccos((np.trace(T_est[:3, :3].t() @ T_gt[:3, :3]) - 1) / 2)
if not np.isnan(rre):
rre_meter.update(rre)
hit_ratio = self.evaluate_hit_ratio(xyz0_corr, xyz1_corr, T_gt, thresh=self.config.hit_ratio_thresh)
hit_ratio_meter.update(hit_ratio)
feat_match_ratio.update(hit_ratio > 0.05)
matching_timer.toc()
num_data += 1
torch.cuda.empty_cache()
if batch_idx % 100 == 0 and batch_idx > 0:
logging.info(' '.join([
f"Validation iter {num_data} / {tot_num_data} : Data Loading Time: {data_timer.avg:.3f},",
f"Feature Extraction Time: {feat_timer.avg:.3f}, Matching Time: {matching_timer.avg:.3f},",
f"Loss: {loss_meter.avg:.3f}, RTE: {rte_meter.avg:.3f}, RRE: {rre_meter.avg:.3f},",
f"Hit Ratio: {hit_ratio_meter.avg:.3f}, Feat Match Ratio: {feat_match_ratio.avg:.3f}"
]))
data_timer.reset()
logging.info(' '.join([
f"Final Loss: {loss_meter.avg:.3f}, RTE: {rte_meter.avg:.3f}, RRE: {rre_meter.avg:.3f},",
f"Hit Ratio: {hit_ratio_meter.avg:.3f}, Feat Match Ratio: {feat_match_ratio.avg:.3f}"
]))
return {
"loss": loss_meter.avg,
"rre": rre_meter.avg,
"rte": rte_meter.avg,
'feat_match_ratio': feat_match_ratio.avg,
'hit_ratio': hit_ratio_meter.avg
}